 One of the things that's become a very big deal in education in the last, I would say, particularly the last one to two decades is all the sentences on evidence-based practice, which is perfectly easy to understand and perfectly healthy. But it has a downside, it has a shadow, you might say, which is that we elevate the quote evidence or the data above the experience, above the lived experience, above the interpretations of the people. And this is a huge problem in education today because data actually doesn't mean anything. It has no inherent meaning. I mean, think of it quantitative numbers. It's a number. You know, it's a test score, it's a whatever. It only has meaning when we create meaning. We compare it to something else, we interpret it in a particular way. So what we often obscure in our emphasis on evidence and data is the interpretation of that data. And that interpretation is never neutral. It's never objective. It's always interpretation by somebody. So when you start to kind of unpack the data by itself doesn't mean anything. It's only data as used or interpreted. Then you quickly realize that there's a whole political or power base to this whole process. Who gets to define the data? Who gets to interpret the data? This is what the data means to this person. It may mean something completely different to this person. And that's where the underlying power dynamics will come into play. We don't ask students, you know, so what does this data mean to you? We say, well, here's the data. It's a little bit like the problems we have in teaching science. We often teach science as if it's about the truth. Now that kind of makes the stomach tense of any good scientist who knows darn well the science does not discover the truth. Science creates different and hopefully better interpretations that are more grounded in what we can tell about reality. Less flawed, less internally inconsistent, less logically inconsistent, but never perfect. Scientists are model builders. They build models to help understand. And data without a model is useless. The problem is the model often sits in the background. It's not articulated. And consequently, we see the data for this group of kids versus this group of kids. And someone says, well, obviously, this group is much better. And we totally missed the point that that was actually our predisposition. Before we ever saw the data, we said, well, these kids are pretty disadvantaged and source, you know, they're going to have more difficulty. So then guess what? We see reading scores and their scores are different. So I think that what we've always tried to do to kind of unpack this or loosen it up or make it more a learning process. Is always present data in concert with what we call the ladder of inference. Ladder of inference is a simple way to, with some discipline, say, how do we interpret? We have data. I think that could be numerical data or quality, but it doesn't really matter. Something that represents a direct capture or something we think is where reality is speaking to us. We can call data that, you know, a directly observable phenomenon in some sense. And then say, yeah, but then we always make an interpretation. We then always had further layers of generalizations. How do we acknowledge all that? Another way to say this is, what does it mean to use data reflectively, not habitually or automatically? So that whatever we think is being said by the data, whatever interpretations we make, we make the interpretation explicit. Well, here's the meaning I'm attaching to that, or here's the way I interpret that. I know that sounds mundane, but simply to have a conversation where the data's here, here's how I interpret it. I guarantee you the next person will have a slightly different interpretation, and so will the next person. But to legitimate that the interpretation is a critical part of the process of making use of data in a rigorous way and thereby dissolve this really hidden power of the evidence speaking as if it has a voice, which it doesn't. So when you start to take seriously that data only has meaning as interpreted, it leads to a natural next question. Well then, why don't we open up this process so different people can be looking at the same data and really offering their different interpretations? And the answer to that usually in my experience is pretty simple. It feels dangerous. It feels like we lose control. And then again, you start to reveal this underlying power structure. Who's the weed who's so worried about losing control? Well, you know, if we let everybody say what it means to them, then the data doesn't have any meaning at all. Well, guess what? As I was saying before, the data actually doesn't have any meaning. It's useful, however, it can be useful if part of a serious process of getting out our interpretations. Because it grounds us. It's not that data is meaningless and it's not arbitrary. Please don't misunderstand me. It can ground us. The question is now we got our feet planted on the ground, what do we see? And this person sees this, and this person sees this, and this person over here sees that. But their feet are planted on the ground. I think that it will feel to people like it's messy. But that's often because they think the data is black and white, which of course it's actually not. And it will feel like they're losing control. So another way to say all this is that can you see the data as a tool for enhancing our ability to talk together about meaningful and complex issues, rather than the data by itself being a freestanding entity? Can we learn to use it wisely? Then data starts to become really useful.